Pavement crack detection through a deep-learned asymmetric encoder-decoder convolutional neural network

ABSTRACTCrack detection on roads’ surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact that it does not provide immediate resolution to the issue, understanding the extent of crack damage is essential for the upkeep of roads. This paper presents a novel approach to automatically detecting pavement cracks using the orthoimage generated by a consumer-grade photogrammetric Unmanned Aerial Vehicle (UAV) and a deep learning algorithm. We used an autoencoder Convolutional Neural Network (CNN) to train a dataset full of challenging factors such as road lines and marks, oil and colour spots, and water stains. The model was tested on a dataset of RGB patches of different patterns of cracks and achieved an overall accuracy (OA) and F1 score of about 0.98. The results demonstrate the effectiveness of the proposed method in accurately detecting pavement cracks in challenging real-world conditions. This approach provides an efficient and cost-effective solution for pavement crack detection, that can be used for measuring the road's quality and monitoring it.

• Record URL:
  • https://doi.org/10.1080/10298436.2023.2255359
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/44544515
• Supplemental Notes:
  • © 2023 Informa UK Limited, trading as Taylor & Francis Group 2023. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Fakhri, Seyed Arya
  • Satari Abrovi, Mehran
  • Zakeri, Hamzeh
  • Safdarinezhad, Alireza
  • Fakhri, Arvin
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 2255359
• Serial:
  • International Journal of Pavement Engineering
  • Volume: 24
  • Issue Number: 1
  • Publisher: Taylor & Francis
  • ISSN: 1029-8436
  • Serial URL: http://www.tandf.co.uk/journals/titles/10298436.html

Subject/Index Terms

• TRT Terms: Algorithms; Detection and identification; Drones; Image analysis; Machine learning; Neural networks; Pavement cracking
• Subject Areas: Highways; Pavements;

Filing Info

• Accession Number: 01907956
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 13 2024 10:36AM